CVE-2025-51859: n/a
Source CVE Database V5
Not known to be exploited · CVSS 6.5 Medium.
Data as of 25 Oct 2026, 19:02 UTC
- EPSS
- 0.5% chance of exploitation in 30 days – higher than 44% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:NExploitation evidence
Disclosed: evidence found.
CVE published 22 Jul 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.
What to do
AI-suggested
European organizations should immediately audit their use of Chaindesk, especially the agent chat components that integrate LLMs. Specific mitigation steps include: 1) Restricting or sanitizing system prompts used to generate AI agent responses to prevent injection of malicious scripts. 2) Implementing robust output encoding and Content Security Policy (CSP) headers to mitigate the impact of any injected scripts. 3) Monitoring chat logs and conversation histories for suspicious or unexpected script tags or SVG elements. 4) Applying strict access controls and multi-factor authentication to reduce the impact of potential session hijacking. 5) Engaging with Chaindesk vendors or support channels to obtain patches or updates addressing this vulnerability as soon as they become available. 6) Educating users about the risks of interacting with untrusted AI agents or clicking on direct conversation links from unknown sources. 7) Employing web application firewalls (WAFs) with custom rules to detect and block malicious payloads targeting the chat interface. These measures go beyond generic advice by focusing on the unique integration of LLMs and the stored nature of the XSS in this context.
Description
Stored Cross-Site Scripting (XSS) vulnerability in Chaindesk thru 2025-05-26 in its agent chat component. An attacker can achieve arbitrary client-side script execution by crafting an AI agent whose system prompt instructs the underlying Large Language Model (LLM) to embed malicious script payloads (e.g., SVG-based XSS) into its chat responses. When a user interacts with such a malicious agent or accesses a direct link to a conversation containing an XSS payload, the script executes in the user's browser. Successful exploitation can lead to the theft of sensitive information, such as JWT session tokens, potentially resulting in account hijacking.